An Adaptive Online Prediction Method With Variable Prediction Horizon for Future Driving Cycle of the Vehicle

Abstract

Accurate prior knowledge of future driving cycle is quite essential in many research and applications related to optimal control of the vehicle and transportation, especially for model predictive control-based energy management for hybrid electric vehicles. Therefore, an adaptive online prediction method with variable prediction horizon is proposed for future driving cycle prediction in this paper. In particular, two aspects of efforts have been explored. First, combining Markov chain and Monte Carlo theory, a multi-scale single-step prediction method is proposed and compared with traditional fixed-scale multi-step method, improving by about 7% in prediction accuracy. Second, to further adapt to variable actual driving cycles, online reconstructions of driving cycle and state filling are introduced to guarantee continuous and robust online application; principal component analysis and cluster analysis are employed to adjust realtime prediction horizons for better overall prediction accuracy. In the end, the proposed method is verified by the experiment of hardware-in-loop simulation, showing more than 20% improvement in prediction accuracy than fixed-horizon prediction method, and relatively good robustness and universality in different driving conditions.

Keywords

• Cluster analysis
• driving cycle prediction
• Markov chain
• multi-scale single-step prediction
• principal component analysis
• state-filling